SMART GRID, LOAD MANAGEMENT AND DYNAMIC PRICING FOR ELECTRICITY: - - PowerPoint PPT Presentation
SMART GRID, LOAD MANAGEMENT AND DYNAMIC PRICING FOR ELECTRICITY: FINDINGS FROM A FIELD PROJECT IN SWITZERLAND Barbara Antonioli Mantegazzini Universit della Svizzera italiana, IdEP, Institute of Economics, Lugano, Switzerland, University of
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Barbara Antonioli Mantegazzini Università della Svizzera italiana, IdEP, Institute of Economics, Lugano, Switzerland, University of Applied Sciences of Southern Switzerland, DEASS, Lugano, Switzerland Alessandro Giusti Dalle Molle Institute for Artificial Intelligence, Lugano, Switzerland
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The role of operators and institutions is changing
From fossil to renewables...
Volatility Intermittency Back-up Disruptive technologies
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
Results:
Possible strategies:
prerequisite for ADR programs)
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The primary idea is to optimise grid management by means of a new concept of Smart-Grid which moves from the bottom (local distribution network) to top (global network).
Main goald:
in the local area;
energy production combined with the storage of this energy in EV batteries;
number of independent homes connected to the smart grid;
with a centralised system can be reduced or even avoided
based on an active algorithm on individual homes, governed by simple network rules and parameters in order to reduce the level of complexity of the system.
for the electricity companies.
The economic task of the S2G project was aimed to provide an optimal set of tariff/pricing scenarios useful to empirically test many aspects of the optimization and simulation process, also reflecting players’ expectations about the future development of the local electricity market.
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The price test design: technical assumptions
analysis based on the expectations of the main distributors and production players involved;
Boilers:
hot water consumption (uniform usage). Thermal conductance 2W/K, heating efficiency 100%. Temperature range: 57 to 63 degC, ambient temperature 20 degC.
~200L/day hot water consumption (uniform usage). Thermal conductance 2W/K, heating efficiency 100%. Temperature range: 57 to 63 degC.
EVs:
17 pm, plugged in with a state of charge of 30%.
is unplugged from 7 am to 9 am and plugged in from 4 pm and 6 pm with a state of charge between 50% and 70%.
ISAAC, the Institute that develops the HAC; simulation will use data of its actual use.
Price scheme 1) Time of use (control group) 2) Time of use with dynamic rates (CED) 3) Flat rate with dynamic rates (CED) 4) Real time pricing
Figure 4. Details of S2G selected tariffs Tariff Type Peak Off Peak Dynamic Rates amount when 1 Time-of-Use 14,40 ctsCHF 11,10 ctsCHF 2 Time-of-Use with Peak Time Rebate 14,40 ctsCHF 11,10 ctsCHF 1 CHF/kWh 5 CED – from 7 pm to 8 pm 3 Flat Rate with Peak Time Rebate 12,90 1 CHF/kWh 5 CED – from 7 pm to 8 pm 4 Real Time Prices Spot market prices (energy) and network prices
RTP = spot market + mark up Network tariff: peak and off peak 2013 and 2017
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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How the price test has been ran?
A simulation in which appliances are not controlled by algorithms has been ran. This simulation generates an energy usage curve: for every minute in the month, we compute how much energy the appliances used; note that, because in this simulation appliances are not considering the energy price, the energy usage curve is the same regardless on the price profile. From the energy usage curve, we compute the total energy cost based on the energy price profile.
We run a simulation in which appliances are controlled by algorithms has been completed. This simulation generates an energy usage curve that depends on the price profile, as algorithms attempt, where possible, to shift energy use to low-cost periods. Again, the total energy cost has computed. For each price profile, this yields the energy cost without and with algorithms; the savings results as the difference between these two values. The algorithm has been ran with two definite objectives, each with the same weight of importance:
consumption curve Selected rates have been tested on one single house The algorithm basically does not consider an energy consumption reduction.
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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Price test simulation results (1)
Figure 5. Monthly bill with and without the algorithm in 2013: Boilers (CHF) 2013 Price scheme 1 2 3 4 Boiler_1 Without HAC 25.46 25.46 26.79 24.77 With HAC 23.13 20.89 25.56 17.61 Boiler_2 Without HAC 43.95 43.95 46.05 42.30 With HAC 40.27 36.86 43.00 31.02 Figure 7. Monthly bill with and without the algorithm in 2017: Boilers (CHF) 2017 Price scheme 1 2 3 4 Boiler_1 Without HAC 29.68 29.68 26.79 27.63 With HAC 28.77 27.32 26.79 24.68 Boiler_2 Without HAC 51.19 51.19 46.05 47.43 With HAC 50.27 48.05 46.37 42.91 Figure 6. Monthly bill with and without the algorithm in 2013: EVs (CHF) 2013 Price scheme 1 2 3 4 EV_1 Without HAC 58.7 49.7 50.1 56.1 With HAC 38.9 28.1 35.2 30.5 EV_2 Without HAC 20.8 20.8 19.7 22.9 With HAC 17.0 9.1 13.3 12.1 EV_3 Without HAC 13.8 13.8 13.4 14.8 With HAC 12.6 7.4 8.6 10.3 Figure 8. Monthly bill with and without the algorithm in 2017: EVs (CHF) 2017 Price scheme 1 2 3 4 EV_1 No HAC 57.5 57.5 50.1 56.7 with HAC 51.0 39.0 38.2 45.1 EV_2 No HAC 23.9 23.9 19.7 22.7 with HAC 20.1 15.5 15.2 17.1 EV_3 No HAC 16.0 15.9 13.4 14.7 with HAC 14.6 13.3 12.0 12.5 7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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Price test simulation results (2)
Figure 9. Monthly savings with and without the algorithm: boilers (2013 and 2017) Figure 10. Monthly savings with and without the algorithm: EVs (2013 and 2017)
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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In general, we can notice that:
higher savings in terms of monthly bills could be obtained with an appropriate management of
EVs charging;
in general, results could be a little underestimated due to the invariance in total consumption; most interesting price schemes seem to be ToU combined with dynamic rates (PTR) and Real
Time Prices;
in particular, RTPs seem to privilege boilers. Savings for EVs are remarkable; due to their strong
flexibility in terms of use they could give back higher price advantages;
again, for EVs the incidence of PTR rewards is very relevant, in certain cases higher than for
boiler; this because at 7 p.m. EVs without algorithm are usually plugged in. The baseline is so very high as much potential savings with HAC;
results seem to confirm the evidence from international pilot projects, with high savings with ToU
combined with dynamic rates (in our case PTR) and RTP;
the role of PTR reward and RTP utilities mark up is critical. In detail, keeping the same mark up,
we need to halve the reward to change results and make the RTP price scheme more attractive;
Monthly savings for boiler and actual usage of EVs seem in line with empirical evidence/pilot
projects.
Price test simulation results (3)
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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We are moving towards a smart electricity market.
Challenges?
design new arrangement for a RES-integrated electricity market describing roles and duties of old
and new relevant market players, as well as potential regulatory framework improvements;
define the most appropriate valorisation of each transaction between market actors. Efficient
market design requires good pricing principles to manage transitions (Newberry, 2017);
Risk allocation between players.
Some suggestions of public policy on the new roles for market players
New, innovative business models are needed:
and climate change challenges (Rogers et al. 2008; Li, Yu, 2014; Musall 2011, Li et al. 2012);
7th Conference on the Regulation of Infrastructures, FSR, 21-22 June 2018
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